A knowledge-based online fault detection method of the assembly process considering the relative poses of components

The real-time process fault detection in the multi-station assembly process is always a challenging problem for auto body manufactures. Traditionally, the fault diagnosis approaches for variation source identification are divided into two categories, i.e. the pattern matching methods and model-based estimation ones based on the collected data set. The measurements provide effective process monitoring, but the real-time process fault diagnosis in the assembly process is still difficult with the traditional diagnosis techniques, and always depends on the engineering experience in practice. Based on the assembly process knowledge, including multi-station assembly hierarchy, fixture scheme, measurement characteristics and tolerances etc. in the multi-station, a knowledge-based diagnostic methodology and procedures are proposed with the measurements of each body in white for part/component defections and faulty assembly station identification. For the station involved with defective parts/components, the sub-coordinate system of the part/component is established reflecting its position and pose in the space, and then the relative pose matrix to the “normally build” pose is calculated based on the deviations of sub-coordinates of the parts in this station. Finally, the assembly process malfunctions are determined by a proposed rule-based strategy with the relative pose matrix in real time. A simple 3 stations assembly process with 5 sheet metal parts was analyzed and compared with the traditional diagnostic method to verify the effectiveness and stability of the proposed method.

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